Abstract
Opinions expressed in online finance-related textual data are having an increasingly profound impact on trading decisions and market movements. This trend highlights the vital role of sentiment analysis as a tool for quantifying the nature and strength of such opinions. With the rapid development of Generative AI (GenAI), supervised fine-tuned (SFT) large language models (LLMs) have become the de facto standard for financial sentiment analysis. However, the SFT paradigm can lead to memorization of the training data and often fails to generalize to unseen samples. This is a critical limitation in financial domains, where models must adapt to previously unobserved events and the nuanced, domain-specific language of finance. To this end, we introduce FinDPO, the first finance-specific LLM framework based on post-training human preference alignment via Direct Preference Optimization (DPO). The proposed FinDPO achieves state-of-the-art performance on standard sentiment classification benchmarks, outperforming existing supervised fine-tuned models by 11% on the average. Uniquely, the FinDPO framework enables the integration of a fine-tuned causal LLM into realistic portfolio strategies through a novel 'logit-to-score' conversion, which transforms discrete sentiment predictions into continuous, rankable sentiment scores (probabilities). In this way, simulations demonstrate that FinDPO is the first sentiment-based approach to maintain substantial positive returns of 67% annually and strong risk-adjusted performance, as indicated by a Sharpe ratio of 2.0, even under realistic transaction costs of 5 basis points (bps).
Abstract (translated)
在线金融相关文本数据中表达的意见对交易决策和市场走势的影响日益显著。这种趋势强调了情感分析作为量化此类意见性质和强度的工具的重要性。随着生成式人工智能（GenAI）的迅速发展，监督微调（SFT）大型语言模型（LLMs）已成为金融市场情感分析的标准方法。然而，SFT范式可能导致对训练数据的记忆化，并且通常无法泛化到未见过的数据样本上。在金融领域中，这种限制尤为关键，因为模型必须适应前所未有的事件并处理金融领域的微妙、特定于行业的语言表达方式。 为此，我们推出了FinDPO——首个基于直接偏好优化（DPO）进行后训练人类偏好数值对齐的金融专用LLM框架。所提出的FinDPO在标准的情感分类基准测试中取得了最先进的性能，在平均精度上超过了现有的监督微调模型11%。独特的是，FinDPO框架通过一种新颖的“logit到评分”转换方法，使经过微调的因果LLM能够整合进实际的投资组合策略中，这种转换将离散的情感预测转化为连续、可排名的情感得分（概率）。这种方式使得模拟结果表明，FinDPO是首个能在每年保持显著正回报率（67%）和强大的风险调整后表现的情况下，采用情感基础方法，并且即使在现实的交易成本为5个基点时也能做到这一点，其夏普比率为2.0。
URL
https://arxiv.org/abs/2507.18417
